Skip to content
This repository has been archived by the owner on Jan 17, 2022. It is now read-only.

semnan-university-ai/Audiology

BranchesTags

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

3 Commits
LICENSE
LICENSE
 
 
README.md
README.md
 
 
audiology2.csv
audiology2.csv
 
 
audiology2.html
audiology2.html
 
 
audiology2.ipynb
audiology2.ipynb
 
 
audiology2.pdf
audiology2.pdf
 
 
audiology2.py
audiology2.py
 
 
output_28_0.png
output_28_0.png
 
 

Repository files navigation

Author : Amir Shokri
github link : https://github.com/amirshnll/Audiology
dataset link : http://archive.ics.uci.edu/ml/datasets/Audiology+%28Original%29
email : amirsh.nll@gmail.com
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from io import StringIO
from sklearn.preprocessing import LabelEncoder
import numpy as np
import seaborn as sns
audio = pd.read_csv('audiology2.csv',header=0,sep=',')
audio.columns
df=audio
df=df.drop("number_row",axis = 1)
var_mod = ['age_gt_60','airBoneGap','boneAbnormal','history_buzzing','history_dizziness',
       'history_fluctuating', 'history_fullness', 'history_heredity',
       'history_nausea', 'history_noise', 'history_recruitment',
       'history_ringing', 'history_roaring', 'history_vomiting',
       'late_wave_poor', 'm_at_2k', 'm_cond_lt_1k', 'm_gt_1k', 'm_m_gt_2k',
       'm_m_sn', 'm_m_sn_gt_1k', 'm_m_sn_gt_2k', 'm_m_sn_gt_500',
       'm_p_sn_gt_2k', 'm_s_gt_500', 'm_s_sn', 'm_s_sn_gt_1k', 'm_s_sn_gt_2k',
       'm_s_sn_gt_3k', 'm_s_sn_gt_4k', 'm_sn_2_3k', 'm_sn_gt_1k', 'm_sn_gt_2k',
       'm_sn_gt_3k', 'm_sn_gt_4k', 'm_sn_gt_500', 'm_sn_gt_6k', 'm_sn_lt_1k',
       'm_sn_lt_2k', 'm_sn_lt_3k', 'middle_wave_poor', 'mod_gt_4k',
       'mod_mixed', 'mod_s_mixed', 'mod_s_sn_gt_500', 'mod_sn', 'mod_sn_gt_1k',
       'mod_sn_gt_2k', 'mod_sn_gt_3k', 'mod_sn_gt_4k', 'mod_sn_gt_500',
       'notch_4k', 'notch_at_4k', 's_sn_gt_1k',
       's_sn_gt_2k', 's_sn_gt_4k', 'static_normal','viith_nerve_signs',
      'wave_V_delayed', 'waveform_ItoV_prolonged']

le = LabelEncoder()

for i in var_mod:

    df[i] = le.fit_transform(df[i])

df.head(10)
<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } 
.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}
</style>
age_gt_60 air() airBoneGap ar_c() ar_u() bone() boneAbnormal bser() history_buzzing history_dizziness ... s_sn_gt_1k s_sn_gt_2k s_sn_gt_4k speech() static_normal tymp() viith_nerve_signs wave_V_delayed waveform_ItoV_prolonged classification
0 0 moderate 0 normal normal ? 1 ? 0 0 ... 0 0 0 normal 1 a 0 0 0 cochlear_unknown
1 1 mild 1 ? absent mild 1 ? 0 0 ... 0 0 0 normal 1 as 0 0 0 mixed_cochlear_age_fixation
2 1 mild 1 ? absent mild 0 ? 0 0 ... 0 0 0 normal 1 b 0 0 0 mixed_cochlear_age_otitis_media
3 1 mild 0 normal normal mild 1 ? 0 0 ... 0 0 0 good 1 a 0 0 0 cochlear_age
4 1 mild 0 normal normal mild 1 ? 0 0 ... 0 0 0 very_good 1 a 0 0 0 cochlear_age
5 0 mild 0 normal normal mild 1 ? 0 0 ... 0 0 0 good 1 a 0 0 0 cochlear_unknown
6 0 mild 0 normal normal mild 1 ? 0 0 ... 0 0 0 very_good 1 a 0 0 0 cochlear_unknown
7 0 severe 0 ? ? ? 1 ? 0 0 ... 0 0 0 ? 1 a 0 0 0 cochlear_unknown
8 1 mild 0 elevated absent mild 1 ? 0 0 ... 0 0 0 good 1 a 0 0 0 cochlear_age
9 1 mild 0 normal absent mild 1 ? 0 0 ... 0 0 0 very_good 1 a 0 0 0 cochlear_age

10 rows × 70 columns

df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 200 entries, 0 to 199
Data columns (total 70 columns):
 #   Column                   Non-Null Count  Dtype 
---  ------                   --------------  ----- 
 0   age_gt_60                200 non-null    int32 
 1   air()                    200 non-null    object
 2   airBoneGap               200 non-null    int32 
 3   ar_c()                   200 non-null    object
 4   ar_u()                   200 non-null    object
 5   bone()                   200 non-null    object
 6   boneAbnormal             200 non-null    int32 
 7   bser()                   200 non-null    object
 8   history_buzzing          200 non-null    int32 
 9   history_dizziness        200 non-null    int32 
 10  history_fluctuating      200 non-null    int32 
 11  history_fullness         200 non-null    int32 
 12  history_heredity         200 non-null    int32 
 13  history_nausea           200 non-null    int32 
 14  history_noise            200 non-null    int32 
 15  history_recruitment      200 non-null    int32 
 16  history_ringing          200 non-null    int32 
 17  history_roaring          200 non-null    int32 
 18  history_vomiting         200 non-null    int32 
 19  late_wave_poor           200 non-null    int32 
 20  m_at_2k                  200 non-null    int32 
 21  m_cond_lt_1k             200 non-null    int32 
 22  m_gt_1k                  200 non-null    int32 
 23  m_m_gt_2k                200 non-null    int32 
 24  m_m_sn                   200 non-null    int32 
 25  m_m_sn_gt_1k             200 non-null    int32 
 26  m_m_sn_gt_2k             200 non-null    int32 
 27  m_m_sn_gt_500            200 non-null    int32 
 28  m_p_sn_gt_2k             200 non-null    int32 
 29  m_s_gt_500               200 non-null    int32 
 30  m_s_sn                   200 non-null    int32 
 31  m_s_sn_gt_1k             200 non-null    int32 
 32  m_s_sn_gt_2k             200 non-null    int32 
 33  m_s_sn_gt_3k             200 non-null    int32 
 34  m_s_sn_gt_4k             200 non-null    int32 
 35  m_sn_2_3k                200 non-null    int32 
 36  m_sn_gt_1k               200 non-null    int32 
 37  m_sn_gt_2k               200 non-null    int32 
 38  m_sn_gt_3k               200 non-null    int32 
 39  m_sn_gt_4k               200 non-null    int32 
 40  m_sn_gt_500              200 non-null    int32 
 41  m_sn_gt_6k               200 non-null    int32 
 42  m_sn_lt_1k               200 non-null    int32 
 43  m_sn_lt_2k               200 non-null    int32 
 44  m_sn_lt_3k               200 non-null    int32 
 45  middle_wave_poor         200 non-null    int32 
 46  mod_gt_4k                200 non-null    int32 
 47  mod_mixed                200 non-null    int32 
 48  mod_s_mixed              200 non-null    int32 
 49  mod_s_sn_gt_500          200 non-null    int32 
 50  mod_sn                   200 non-null    int32 
 51  mod_sn_gt_1k             200 non-null    int32 
 52  mod_sn_gt_2k             200 non-null    int32 
 53  mod_sn_gt_3k             200 non-null    int32 
 54  mod_sn_gt_4k             200 non-null    int32 
 55  mod_sn_gt_500            200 non-null    int32 
 56  notch_4k                 200 non-null    int32 
 57  notch_at_4k              200 non-null    int32 
 58  o_ar_c()                 200 non-null    object
 59  o_ar_u()                 200 non-null    object
 60  s_sn_gt_1k               200 non-null    int32 
 61  s_sn_gt_2k               200 non-null    int32 
 62  s_sn_gt_4k               200 non-null    int32 
 63  speech()                 200 non-null    object
 64  static_normal            200 non-null    int32 
 65  tymp()                   200 non-null    object
 66  viith_nerve_signs        200 non-null    int32 
 67  wave_V_delayed           200 non-null    int32 
 68  waveform_ItoV_prolonged  200 non-null    int32 
 69  classification           200 non-null    object
dtypes: int32(60), object(10)
memory usage: 62.6+ KB

df['speech()'].value_counts()
normal        83
good          36
very_good     35
poor          19
very_poor     18
?              6
unmeasured     3
Name: speech(), dtype: int64

le = LabelEncoder()
data_cat=df['speech()']
data_cat_encoded= le.fit_transform(data_cat)
data_cat_encoded= pd.DataFrame(data_cat_encoded,columns=["speech()"])
df['speech()']=data_cat_encoded
df['speech()'].value_counts()
2    83
1    36
5    35
3    19
6    18
0     6
4     3
Name: speech(), dtype: int64

df['air()'].value_counts()
mild        101
normal       73
moderate     17
severe        8
profound      1
Name: air(), dtype: int64

le = LabelEncoder()
data_cat=df['air()']
data_cat_encoded= le.fit_transform(data_cat)
data_cat_encoded= pd.DataFrame(data_cat_encoded,columns=['air()'])
df['air()']=data_cat_encoded
df['air()'].value_counts()
0    101
2     73
1     17
4      8
3      1
Name: air(), dtype: int64

df['ar_c()'].value_counts()
normal      117
absent       50
elevated     29
?             4
Name: ar_c(), dtype: int64

le = LabelEncoder()
data_cat=df['ar_c()']
data_cat_encoded= le.fit_transform(data_cat)
data_cat_encoded= pd.DataFrame(data_cat_encoded,columns=['ar_c()'])
df['ar_c()']=data_cat_encoded
df['ar_c()'].value_counts()
3    117
1     50
2     29
0      4
Name: ar_c(), dtype: int64

le = LabelEncoder()
data_cat=df['ar_u()']
data_cat_encoded= le.fit_transform(data_cat)
data_cat_encoded= pd.DataFrame(data_cat_encoded,columns=['ar_u()'])
df['ar_u()']=data_cat_encoded
df['ar_u()'].value_counts()
3    121
1     41
2     35
0      3
Name: ar_u(), dtype: int64

df['bone()'].value_counts()
?             75
mild          55
normal        37
unmeasured    30
moderate       3
Name: bone(), dtype: int64

le = LabelEncoder()
data_cat=df['bone()']
data_cat_encoded= le.fit_transform(data_cat)
data_cat_encoded= pd.DataFrame(data_cat_encoded,columns=['bone()'])
df['bone()']=data_cat_encoded
df['bone()'].value_counts()
0    75
1    55
3    37
4    30
2     3
Name: bone(), dtype: int64

df['bser()'].value_counts()
?           196
degraded      2
normal        2
Name: bser(), dtype: int64

le = LabelEncoder()
data_cat=df['bser()']
data_cat_encoded= le.fit_transform(data_cat)
data_cat_encoded= pd.DataFrame(data_cat_encoded,columns=['bser()'])
df['bser()']=data_cat_encoded
df['bser()'].value_counts()
0    196
2      2
1      2
Name: bser(), dtype: int64

df['o_ar_c()'].value_counts()
le = LabelEncoder()
data_cat=df['o_ar_c()']
data_cat_encoded= le.fit_transform(data_cat)
data_cat_encoded= pd.DataFrame(data_cat_encoded,columns=['o_ar_c()'])
df['o_ar_c()']=data_cat_encoded
df['o_ar_c()'].value_counts()
3    124
1     47
2     24
0      5
Name: o_ar_c(), dtype: int64

df['o_ar_u()'].value_counts()
le = LabelEncoder()
data_cat=df['o_ar_u()']
data_cat_encoded= le.fit_transform(data_cat)
data_cat_encoded= pd.DataFrame(data_cat_encoded,columns=['o_ar_u()'])
df['o_ar_u()']=data_cat_encoded
df['o_ar_u()'].value_counts()
3    114
1     47
2     37
0      2
Name: o_ar_u(), dtype: int64

df['tymp()'].value_counts()
le = LabelEncoder()
data_cat=df['tymp()']
data_cat_encoded= le.fit_transform(data_cat)
data_cat_encoded= pd.DataFrame(data_cat_encoded,columns=['tymp()'])
df['tymp()']=data_cat_encoded
df['tymp()'].value_counts()
0    169
2     12
3      8
4      7
1      4
Name: tymp(), dtype: int64

df['tymp()'].value_counts()
0    169
2     12
3      8
4      7
1      4
Name: tymp(), dtype: int64

le = LabelEncoder()
data_cat=df['tymp()']
data_cat_encoded= le.fit_transform(data_cat)
data_cat_encoded= pd.DataFrame(data_cat_encoded,columns=['tymp()'])
df['tymp()']=data_cat_encoded
df['tymp()'].value_counts()
0    169
2     12
3      8
4      7
1      4
Name: tymp(), dtype: int64

df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 200 entries, 0 to 199
Data columns (total 70 columns):
 #   Column                   Non-Null Count  Dtype 
---  ------                   --------------  ----- 
 0   age_gt_60                200 non-null    int32 
 1   air()                    200 non-null    int32 
 2   airBoneGap               200 non-null    int32 
 3   ar_c()                   200 non-null    int32 
 4   ar_u()                   200 non-null    int32 
 5   bone()                   200 non-null    int32 
 6   boneAbnormal             200 non-null    int32 
 7   bser()                   200 non-null    int32 
 8   history_buzzing          200 non-null    int32 
 9   history_dizziness        200 non-null    int32 
 10  history_fluctuating      200 non-null    int32 
 11  history_fullness         200 non-null    int32 
 12  history_heredity         200 non-null    int32 
 13  history_nausea           200 non-null    int32 
 14  history_noise            200 non-null    int32 
 15  history_recruitment      200 non-null    int32 
 16  history_ringing          200 non-null    int32 
 17  history_roaring          200 non-null    int32 
 18  history_vomiting         200 non-null    int32 
 19  late_wave_poor           200 non-null    int32 
 20  m_at_2k                  200 non-null    int32 
 21  m_cond_lt_1k             200 non-null    int32 
 22  m_gt_1k                  200 non-null    int32 
 23  m_m_gt_2k                200 non-null    int32 
 24  m_m_sn                   200 non-null    int32 
 25  m_m_sn_gt_1k             200 non-null    int32 
 26  m_m_sn_gt_2k             200 non-null    int32 
 27  m_m_sn_gt_500            200 non-null    int32 
 28  m_p_sn_gt_2k             200 non-null    int32 
 29  m_s_gt_500               200 non-null    int32 
 30  m_s_sn                   200 non-null    int32 
 31  m_s_sn_gt_1k             200 non-null    int32 
 32  m_s_sn_gt_2k             200 non-null    int32 
 33  m_s_sn_gt_3k             200 non-null    int32 
 34  m_s_sn_gt_4k             200 non-null    int32 
 35  m_sn_2_3k                200 non-null    int32 
 36  m_sn_gt_1k               200 non-null    int32 
 37  m_sn_gt_2k               200 non-null    int32 
 38  m_sn_gt_3k               200 non-null    int32 
 39  m_sn_gt_4k               200 non-null    int32 
 40  m_sn_gt_500              200 non-null    int32 
 41  m_sn_gt_6k               200 non-null    int32 
 42  m_sn_lt_1k               200 non-null    int32 
 43  m_sn_lt_2k               200 non-null    int32 
 44  m_sn_lt_3k               200 non-null    int32 
 45  middle_wave_poor         200 non-null    int32 
 46  mod_gt_4k                200 non-null    int32 
 47  mod_mixed                200 non-null    int32 
 48  mod_s_mixed              200 non-null    int32 
 49  mod_s_sn_gt_500          200 non-null    int32 
 50  mod_sn                   200 non-null    int32 
 51  mod_sn_gt_1k             200 non-null    int32 
 52  mod_sn_gt_2k             200 non-null    int32 
 53  mod_sn_gt_3k             200 non-null    int32 
 54  mod_sn_gt_4k             200 non-null    int32 
 55  mod_sn_gt_500            200 non-null    int32 
 56  notch_4k                 200 non-null    int32 
 57  notch_at_4k              200 non-null    int32 
 58  o_ar_c()                 200 non-null    int32 
 59  o_ar_u()                 200 non-null    int32 
 60  s_sn_gt_1k               200 non-null    int32 
 61  s_sn_gt_2k               200 non-null    int32 
 62  s_sn_gt_4k               200 non-null    int32 
 63  speech()                 200 non-null    int32 
 64  static_normal            200 non-null    int32 
 65  tymp()                   200 non-null    int64 
 66  viith_nerve_signs        200 non-null    int32 
 67  wave_V_delayed           200 non-null    int32 
 68  waveform_ItoV_prolonged  200 non-null    int32 
 69  classification           200 non-null    object
dtypes: int32(68), int64(1), object(1)
memory usage: 56.4+ KB

le = LabelEncoder()
data_cat=df['classification']
data_cat_encoded= le.fit_transform(data_cat)
data_cat_encoded= pd.DataFrame(data_cat_encoded,columns=['classification'])
df['classification']=data_cat_encoded
df['classification'].value_counts()
7     48
2     46
18    20
3     18
6     16
22     8
9      6
14     5
11     4
21     4
19     4
15     3
5      2
8      2
23     2
12     2
13     2
17     2
10     1
16     1
4      1
20     1
1      1
0      1
Name: classification, dtype: int64

df_label=df["classification"].copy()
df=df.drop("bser()",axis = 1)
from sklearn.preprocessing import StandardScaler

#feature_scal = StandardScaler()
#df = pd.DataFrame(feature_scal.fit_transform(df), columns=df.columns)
#df.head()
y=df.classification
x = df.drop(columns=['classification'])
df=df.drop("classification",axis = 1)
median = df['mod_sn'].median()
df['mod_sn'].fillna(median)
df.head(50)
<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } 
.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}
</style>
age_gt_60 air() airBoneGap ar_c() ar_u() bone() boneAbnormal history_buzzing history_dizziness history_fluctuating ... o_ar_u() s_sn_gt_1k s_sn_gt_2k s_sn_gt_4k speech() static_normal tymp() viith_nerve_signs wave_V_delayed waveform_ItoV_prolonged
0 0 1 0 3 3 0 1 0 0 0 ... 3 0 0 0 2 1 0 0 0 0
1 1 0 1 0 1 1 1 0 0 0 ... 1 0 0 0 2 1 2 0 0 0
2 1 0 1 0 1 1 0 0 0 0 ... 1 0 0 0 2 1 3 0 0 0
3 1 0 0 3 3 1 1 0 0 0 ... 3 0 0 0 1 1 0 0 0 0
4 1 0 0 3 3 1 1 0 0 0 ... 3 0 0 0 5 1 0 0 0 0
5 0 0 0 3 3 1 1 0 0 0 ... 3 0 0 0 1 1 0 0 0 0
6 0 0 0 3 3 1 1 0 0 0 ... 3 0 0 0 5 1 0 0 0 0
7 0 4 0 0 0 0 1 0 0 0 ... 0 0 0 0 0 1 0 0 0 0
8 1 0 0 2 1 1 1 0 0 0 ... 1 0 0 0 1 1 0 0 0 0
9 1 0 0 3 1 1 1 0 0 0 ... 1 0 0 0 5 1 0 0 0 0
10 1 0 0 3 3 0 1 0 0 0 ... 3 0 0 0 1 1 0 0 0 0
11 0 4 0 3 3 2 1 0 0 0 ... 3 0 0 0 6 1 0 0 0 0
12 0 2 0 2 3 1 0 0 0 0 ... 2 0 0 0 2 1 0 0 0 0
13 1 0 0 3 3 1 1 0 0 0 ... 2 0 0 0 5 1 0 0 0 0
14 1 0 0 3 2 1 1 0 0 0 ... 2 0 0 0 1 1 0 0 0 0
15 0 0 0 3 3 1 1 0 0 0 ... 2 0 0 0 5 1 0 0 0 0
16 0 0 0 3 2 1 1 0 0 0 ... 3 0 0 0 2 1 0 0 0 0
17 0 2 0 3 3 0 0 0 0 0 ... 2 0 0 0 2 1 0 0 0 0
18 0 2 0 3 2 0 0 0 0 0 ... 3 0 0 0 2 1 0 0 0 0
19 1 0 0 3 3 0 1 0 0 0 ... 3 1 0 0 6 1 0 0 0 0
20 1 0 0 3 3 0 1 0 0 0 ... 3 0 1 0 2 1 0 0 0 0
21 0 2 0 3 3 0 1 0 0 0 ... 3 0 1 0 1 1 0 0 0 0
22 0 2 0 3 3 3 0 0 0 0 ... 3 0 0 0 2 1 0 0 0 0
23 0 1 0 2 2 0 1 0 0 0 ... 3 0 0 0 6 1 0 0 0 0
24 0 2 0 3 3 3 1 0 0 0 ... 2 0 0 0 5 1 0 0 0 0
25 0 2 0 3 2 3 1 0 0 0 ... 3 0 0 0 5 1 0 0 0 0
26 0 0 0 3 3 0 1 0 0 0 ... 3 0 0 1 2 1 0 0 0 0
27 0 0 0 1 1 1 1 0 0 0 ... 3 0 0 0 6 1 0 0 0 0
28 0 0 0 2 3 1 1 0 0 0 ... 1 0 0 0 2 1 0 0 0 0
29 0 0 0 3 3 0 0 0 0 0 ... 3 0 0 0 2 1 0 0 0 0
30 0 2 0 2 3 0 0 0 0 0 ... 3 0 0 0 2 1 0 0 0 0
31 0 2 0 3 2 0 0 0 0 0 ... 3 0 0 0 1 1 0 0 0 0
32 0 2 0 2 2 0 0 0 0 0 ... 3 0 0 0 1 1 0 0 0 0
33 0 1 1 1 1 1 1 0 0 0 ... 3 0 0 0 5 1 3 0 0 0
34 0 0 0 1 3 1 1 0 0 0 ... 1 0 0 0 2 1 0 0 0 0
35 1 0 0 1 1 1 1 0 0 0 ... 1 0 0 0 5 1 0 0 0 0
36 0 2 0 1 2 3 0 0 0 0 ... 1 0 0 0 2 1 0 0 0 0
37 0 4 1 1 1 1 0 0 0 0 ... 2 0 0 0 6 1 1 0 0 0
38 1 2 0 3 3 0 0 0 0 0 ... 2 0 0 0 1 1 0 0 0 0
39 1 2 0 2 3 0 0 0 0 0 ... 3 0 0 0 5 1 0 0 0 0
40 0 2 0 3 2 3 0 0 0 0 ... 1 0 0 0 2 1 0 0 0 0
41 1 0 0 3 3 0 0 0 0 0 ... 3 0 0 0 2 1 0 0 0 0
42 0 0 0 2 3 0 0 0 0 0 ... 2 0 0 0 2 1 0 0 0 0
43 0 0 0 2 2 0 0 0 0 0 ... 3 0 0 0 2 1 0 0 0 0
44 0 1 0 1 1 2 1 1 0 0 ... 2 0 0 0 6 1 0 0 1 0
45 0 0 1 1 1 3 0 0 0 0 ... 1 0 0 0 0 1 2 0 0 0
46 0 0 0 1 1 1 1 0 0 0 ... 2 0 0 0 2 1 0 0 0 0
47 0 2 0 1 2 3 0 0 0 0 ... 1 0 0 0 2 1 0 0 0 0
48 1 1 0 3 3 0 0 0 0 0 ... 2 0 1 0 6 0 0 0 0 0
49 1 1 0 3 2 0 0 0 0 0 ... 3 0 1 0 6 0 0 0 0 0

50 rows × 68 columns

x_train,x_test,y_train,y_test =  train_test_split(x,y,test_size=0.4,random_state=400)
from sklearn.tree import DecisionTreeClassifier
from sklearn import metrics
from sklearn import preprocessing
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
clft=DecisionTreeClassifier()
clft=clft.fit(x_train,y_train)
y_predt = clft.predict(x_test)


from sklearn import tree
plt.figure(figsize=(50,80))
temp = tree.plot_tree(clft.fit(x,y),fontsize=24)
plt.show()

png

from sklearn.naive_bayes import GaussianNB
clfb = GaussianNB()
clfb.fit(x_train,y_train.ravel())
y_predb = clfb.predict(x_test)
print(classification_report(y_test,clfb.predict(x_test)))
              precision    recall  f1-score   support

           2       0.95      0.91      0.93        22
           3       0.80      0.80      0.80        10
           6       1.00      1.00      1.00         4
           7       0.86      0.75      0.80        16
           9       1.00      1.00      1.00         4
          11       0.25      1.00      0.40         1
          14       1.00      0.67      0.80         3
          15       1.00      1.00      1.00         1
          17       1.00      1.00      1.00         1
          18       0.80      0.80      0.80        10
          19       0.00      0.00      0.00         3
          21       0.00      0.00      0.00         2
          22       0.33      1.00      0.50         3

    accuracy                           0.80        80
   macro avg       0.69      0.76      0.69        80
weighted avg       0.81      0.80      0.79        80



C:\Users\Amirshnll\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

from sklearn.neighbors import KNeighborsClassifier
k=1
clfk= KNeighborsClassifier(n_neighbors=k)
clfk.fit(x_train,y_train.ravel())
y_predk=clfk.predict(x_test)
print("when k = {} neighbors , knn test acuracy : {}" .format(k,clfk.score(x_test,y_test)))
print("when k = {} neighbors , knn test acuracy : {}" .format(k,clfk.score(x_train,y_train))) 
print(classification_report(y_test,clfk.predict(x_test)))
ran = np.arange(1,30)
train_list = []
test_list = []
for i,each in enumerate(ran):
    clfk= KNeighborsClassifier(n_neighbors=each)
    clfk.fit(x_train,y_train.ravel())
    
when k = 1 neighbors , knn test acuracy : 0.6125
when k = 1 neighbors , knn test acuracy : 1.0
              precision    recall  f1-score   support

           2       0.71      0.91      0.80        22
           3       1.00      0.50      0.67        10
           6       0.33      0.50      0.40         4
           7       0.44      0.50      0.47        16
           8       0.00      0.00      0.00         0
           9       1.00      0.75      0.86         4
          11       0.50      1.00      0.67         1
          14       0.60      1.00      0.75         3
          15       0.00      0.00      0.00         1
          17       1.00      1.00      1.00         1
          18       0.50      0.50      0.50        10
          19       0.00      0.00      0.00         3
          21       0.00      0.00      0.00         2
          22       1.00      0.33      0.50         3

    accuracy                           0.61        80
   macro avg       0.51      0.50      0.47        80
weighted avg       0.62      0.61      0.59        80



C:\Users\Amirshnll\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
C:\Users\Amirshnll\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1221: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

from sklearn.neural_network import MLPClassifier
clfm = MLPClassifier(hidden_layer_sizes=(5,),max_iter=1500)
clfm.fit(x_train,y_train.ravel())
y_predm = clfm.predict(x_test)
print ("acuracy:", metrics.accuracy_score (y_test,y_predm))
acuracy: 0.7625


C:\Users\Amirshnll\anaconda3\lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1500) reached and the optimization hasn't converged yet.
  warnings.warn(



   



        


  
      

      

        

          

            

  
About


      

        Audiology Data Set
      

      

        
        
          archive.ics.uci.edu/ml/datasets/Audiology+%28Original%29
        
      


    
Topics

    

        

      
  ml

      
  uci

      
  data-set

      
  audiology

  


    


    
Resources

    

      
        
        Readme
    


  
    
License

  

    
      
     MIT license
    
  





  
  

  

    
      
      Activity
  


    

      
        
        Custom properties
    


  
Stars

  

    
      
      1
      star
  


  
Watchers

  

    
      
      2
      watching
  


  
Forks

  

    
      
      0
      forks
  


    

      
          Report repository
    




          

        


        
            

              

                

  
    Releases
      1



  
    
    

      

        1.0
        
          Latest
      

      
Jan 30, 2021

    


              

            


        
        
        
            

        
        
        
            

              

                
Languages